{"id":"https://openalex.org/W7133548662","doi":"https://doi.org/10.48550/arxiv.2603.02725","title":"S2CDR: Smoothing-Sharpening Process Model for Cross-Domain Recommendation","display_name":"S2CDR: Smoothing-Sharpening Process Model for Cross-Domain Recommendation","publication_year":2026,"publication_date":"2026-03-03","ids":{"openalex":"https://openalex.org/W7133548662","doi":"https://doi.org/10.48550/arxiv.2603.02725"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.02725","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.02725","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.02725","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128067704","display_name":"XiaoDong Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Li, Xiaodong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009392739","display_name":"Juwei Yue","orcid":"https://orcid.org/0000-0002-6899-5724"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yue, Juwei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128064081","display_name":"Xinghua Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Xinghua","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128118283","display_name":"Jiawei Sheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sheng, Jiawei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128094072","display_name":"Wenyuan Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Wenyuan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059851244","display_name":"Taoyu Su","orcid":"https://orcid.org/0009-0003-1674-7635"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Su, Taoyu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128089197","display_name":"Zefeng Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Zefeng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5125039698","display_name":"Tingwen Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Tingwen","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5128067704"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9614999890327454,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10203","display_name":"Recommender Systems and Techniques","score":0.9614999890327454,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.010999999940395355,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.0026000000070780516,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.6854000091552734},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5160999894142151},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.49799999594688416},{"id":"https://openalex.org/keywords/sharpening","display_name":"Sharpening","score":0.4950000047683716},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.492000013589859},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.46070000529289246},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.39899998903274536},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.39570000767707825}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6906999945640564},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.6854000091552734},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5160999894142151},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.49799999594688416},{"id":"https://openalex.org/C2781137444","wikidata":"https://www.wikidata.org/wiki/Q237105","display_name":"Sharpening","level":2,"score":0.4950000047683716},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.492000013589859},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.46070000529289246},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.39899998903274536},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.39570000767707825},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3840000033378601},{"id":"https://openalex.org/C21569690","wikidata":"https://www.wikidata.org/wiki/Q94702","display_name":"Collaborative filtering","level":3,"score":0.37700000405311584},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3727000057697296},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.3684000074863434},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.36649999022483826},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3521000146865845},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.3237000107765198},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3174999952316284},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.31369999051094055},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3118000030517578},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.2964000105857849},{"id":"https://openalex.org/C76956256","wikidata":"https://www.wikidata.org/wiki/Q27610560","display_name":"Process modeling","level":3,"score":0.26840001344680786},{"id":"https://openalex.org/C174998907","wikidata":"https://www.wikidata.org/wiki/Q357662","display_name":"Work in process","level":2,"score":0.26840001344680786},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.26739999651908875},{"id":"https://openalex.org/C121194460","wikidata":"https://www.wikidata.org/wiki/Q856741","display_name":"Random walk","level":2,"score":0.2538999915122986}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.02725","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.02725","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.02725","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.02725","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.7926310896873474,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"User":[0],"cold-start":[1,24,98,156],"problem":[2],"is":[3,109],"a":[4,17,88,105,138,227],"long-standing":[5],"challenge":[6],"in":[7,61,77,137,201,226],"recommendation":[8,12],"systems.":[9],"Fortunately,":[10],"cross-domain":[11],"(CDR)":[13],"has":[14],"emerged":[15],"as":[16,101],"highly":[18],"effective":[19],"remedy":[20],"for":[21,95,155,159,196],"the":[22,51,57,62,75,119,124,142,147,152,160,165,169,176,185,192,204],"user":[23,79],"challenge,":[25],"with":[26,43,111,203],"recently":[27],"developed":[28],"diffusion":[29,66],"models":[30,67],"(DMs)":[31],"demonstrating":[32],"exceptional":[33],"performance.":[34],"However,":[35],"these":[36],"DMs-based":[37],"CDR":[38,96,215],"methods":[39,225],"focus":[40],"on":[41,168,212],"dealing":[42],"user-item":[44],"interactions,":[45],"overlooking":[46],"correlations":[47,177],"between":[48,178],"items":[49,179],"across":[50,81,180],"source":[52],"and":[53,108,141,182],"target":[54],"domains.":[55,82],"Meanwhile,":[56],"Gaussian":[58],"noise":[59,194],"added":[60],"forward":[63],"process":[64,121,144],"of":[65,91],"would":[68],"hurt":[69],"user's":[70,198],"personalized":[71],"preference,":[72,200],"leading":[73],"to":[74,97,113,150,173,189],"difficulty":[76],"transferring":[78],"preference":[80,135,148],"To":[83],"this":[84],"end,":[85],"we":[86,163],"propose":[87],"novel":[89],"paradigm":[90],"Smoothing-Sharpening":[92],"Process":[93],"Model":[94],"users,":[99],"termed":[100],"S2CDR":[102,220],"which":[103],"features":[104],"corruption-recovery":[106],"architecture":[107],"solved":[110],"respect":[112],"ordinary":[114],"differential":[115],"equations":[116],"(ODEs).":[117],"Specifically,":[118],"smoothing":[120,161],"gradually":[122],"corrupts":[123],"original":[125],"user-item/item-item":[126],"interaction":[127],"matrices":[128],"derived":[129],"from":[130],"both":[131],"domains":[132],"into":[133],"smoothed":[134],"signals":[136,149],"noise-free":[139],"manner,":[140],"sharpening":[143],"iteratively":[145],"sharpens":[146],"recover":[151],"unknown":[153],"interactions":[154],"users.":[157],"Wherein,":[158],"process,":[162],"introduce":[164],"heat":[166],"equation":[167],"item-item":[170],"similarity":[171],"graph":[172,205],"better":[174],"capture":[175],"domains,":[181],"further":[183],"build":[184],"tailor-designed":[186],"low-pass":[187],"filter":[188,190],"out":[191],"high-frequency":[193],"information":[195],"capturing":[197],"intrinsic":[199],"accordance":[202],"signal":[206],"processing":[207],"(GSP)":[208],"theory.":[209],"Extensive":[210],"experiments":[211],"three":[213],"real-world":[214],"scenarios":[216],"confirm":[217],"that":[218],"our":[219],"significantly":[221],"outperforms":[222],"previous":[223],"SOTA":[224],"training-free":[228],"manner.":[229]},"counts_by_year":[],"updated_date":"2026-03-05T07:36:02.291473","created_date":"2026-03-05T00:00:00"}